class_docstr: A sampler worker for Task Embedding Algorithm.
  
  In addition to DefaultWorker, this worker adds one-hot task id to env_info,
  and adds latent and latent infos to agent_info.
  
  Args:
      seed (int): The seed to use to intialize random number generators.
      max_episode_length (int or float): The maximum length of episodes to
          sample. Can be (floating point) infinity.
      worker_number(int): The number of the worker where this update is
          occurring. This argument is used to set a different seed for each
          worker.
  
  Attributes:
      agent (Policy or None): The worker's agent.
      env (Environment or None): The worker's environment.
docstr: Begin a new episode.
docstr_tok: ["Begin","a","new","episode","."]
code: def start_episode(self):
      """Begin a new episode."""
      # pylint: disable=protected-access
      self._t = self.env._active_task_one_hot()
      self._z, self._latent_info = self.agent.get_latent(self._t)
      self._z = self.agent.latent_space.flatten(self._z)
      super().start_episode()
  
file_path: garage/src/garage/tf/algos/te.py
imports: from collections import defaultdict
  import numpy
  from garage import EpisodeBatch, StepType
  from garage.sampler import DefaultWorker
label_desc: Use image node_id 2 for calling the TaskEmbeddingWorker obj's underlying member method code with example usage: obj.start_episode() without return types